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MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artistic Composition for Image Creation
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The Design of High-Level Features for Photo Quality Assessment
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
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Proceedings of the 17th international conference on World Wide Web
Photo and Video Quality Evaluation: Focusing on the Subject
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Online Display Advertising: Targeting and Obtrusiveness
Marketing Science
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PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image quality assessment based on a degradation model
IEEE Transactions on Image Processing
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One of the most important categories of online advertising is display advertising which provides publishers with significant revenue. Similar to other categories, the main goal in display advertising is to maximize user response rate for advertising campaigns, such as click through rates (CTR) or conversion rates. Previous studies have tried to optimize these parameters using objectives such as behavioral targeting. However, there is no published work so far to address the effect of the visual appearance of ads (creatives) on user response rate via a systematic data-driven approach. In this paper, we quantitatively study the relationship between the visual appearance and performance of creatives using large scale data in the world's largest display ads exchange system, RightMedia. We designed a set of 43 visual features, some of which are novel and others are inspired by related work. We extracted these features from real creatives served on RightMedia. We also designed and conducted a series of experiments to evaluate the effectiveness of visual features for CTR prediction, ranking and performance classification. Based on the evaluation results, we selected a subset of features that have the highest impact on CTR. We believe that the findings presented in this paper will be very useful for the online advertising industry in designing high-performance creatives. It also provides the research community with the first ever data set, initial insights into visual appearance's effect on user response propensity, and evaluation benchmarks for further study.